Shorebirds have been banded for decades and monitoring programs have helped to accumulate large band-encounter datasets from across the globe; however, many of these datasets are left largely unused, particularly those collected by citizen scientists. These datasets can provide valuable insight into the migration and movement strategies of shorebirds and the threats they face throughout their migratory cycle. We used long-term (2003–2018) band-encounter data of Red Knots Calidris canutus rufa in North America to determine: (1) the spatiotemporal distribution during the nonbreeding season, (2) site fidelity to nonbreeding sites, and (3) migratory connectivity of knots using the southeastern United States (Southeast), an important overwintering and stopover area for this subspecies. Annual mean site fidelity ranged from 0% to 86% across 24 sites. We found movement between sites across the Southeast during migratory and wintering periods, indicating that knots are using the region as interconnected sites, as opposed to relying on a single site or a cluster of adjacent sites. We identified ‘hop migration’ as a common strategy for knots in the region, and showed regular within-year movement between sites in South Carolina, Georgia, and Florida. The Southeast is an understudied part of the rufa range; our results show the importance of the region to the subspecies both as a stopover and wintering area. Despite the inherent biases in the data and imperfect detection due to inconsistent survey effort, the data showed large-scale movements and confirmed the region as a complex of sites connected by knots.
Florida's Big Bend region hosts the second largest concentration of breeding American Oystercatchers in the state, but reproductive success is low. Nest site characteristics and predation were examined to determine their influence on survival of nests and broods at two areas in the southern Big Bend (Cedar Key and Barge Canal). The probability of a nest surviving in Cedar Key was low (x̄ = 0.25, CI = 0.13–0.41) and limited by nest overwash (46% of known nest attempts); survival of nests at Barge Canal was much higher (x̄ = 0.45, CI = 0.31–0.58). However, 40% of chicks that survived to fledge (35 days) at Barge Canal died before reaching independence (60 days). Raccoon presence and hatch date were negatively correlated with brood survival at Barge Canal. Finally, chicks at Barge Canal weighed less and were smaller compared to chicks at an Atlantic Coast site, which may be related to low abundance of live oysters within 100 m of their nest sites. Efforts to enhance oystercatcher reproductive success may require different approaches for each site: habitat restoration to increase elevation of nest sites in Cedar Key and reduction of predators at Barge Canal.
This pamphlet presents a series of general reference maps showing relevant geospatial features of the U.S. Arctic boundary as defined by the U.S. Congress since 1984. The first generation of the U.S. Arctic Research and Policy Act (ARPA) boundary maps was originally formatted and published in 2009 by a private firm contracted with the National Science Foundation and the U.S. Arctic Research Commission. Recognizing the steadily increasing relevance of Arctic issues to national and global affairs that requires more functional projections and online tools, the U.S. Geological Survey (USGS) Alaska Regional Office and the National Geospatial Technical Operations Center developed this updated series of ARPA boundary maps. Map sheet 1 shows the ARPA boundary as it relates to Alaska and marine features of the Bering Sea. Map sheet 2 shows the ARPA boundary from a circumpolar perspective. Map sheet 3 shows the national boundary of the U.S. 200-nautical-mile Exclusive Economic Zone through the Bering, Chukchi, and Beaufort Seas, facilitating Arctic domain awareness and more consistent territorial assessments of the U.S. Arctic. Map sheet 4 shows, in poster-size detail, the ARPA boundary as it relates to terrestrial features of Arctic Alaska north of the Yukon and Kuskokwim Rivers. Map sheet 5 shows, in poster-size detail, the ARPA boundary as it relates to marine and terrestrial features of the Aleutian Islands. These new maps collectively illustrate several value-added attributes, including updated bathymetry and shoreline refinements, demographic information, international borders and offshore territorial claims, Alaska conservation areas, Alaska land cover, Alaska terrestrial shaded relief, annual sea ice maximum extent, annual circumpolar 10-degree-Celsius isotherm, location of active volcanoes, and updated geospatial information. The static PDF-file maps offer value as standalone products but are intended for use with a potential interactive website that can be sourced by annual data updates, allowing users to access the various map layers in a dynamic up-to-date environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3484","usgsCitation":"Williams, D.M., and Richmond, C.L., 2021, Maps of the Arctic Alaska boundary area as defined by the U.S. Arctic Research and Policy Act—Including geospatial characteristics of select marine and terrestrial features: U.S. Geological Survey Scientific Investigations Map 3484, 7 p., 5 sheets, https://doi.org/10.3133/sim3484.","productDescription":"Pamphlet: vi, 7 p.; 5 Sheets: 47.50 × 33.50 inches or smaller","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-125844","costCenters":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"links":[{"id":392220,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet5.pdf","text":"Map sheet 5","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 5","linkHelpText":"— The Arctic Research and Policy Act Region—Aleutian Islands"},{"id":392219,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet4.pdf","text":"Map sheet 4","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 4","linkHelpText":"— The Arctic Research and Policy Act Region—Mainland Alaska"},{"id":392218,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet3.pdf","text":"Map sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 3","linkHelpText":"— The Arctic Research and Policy Act Region—U.S. Territorial Limits"},{"id":392217,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet2.pdf","text":"Map sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 2","linkHelpText":"— The Arctic Research and Policy Act Region—Circumpolar Perspective"},{"id":392216,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet1.pdf","text":"Map sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 1","linkHelpText":"— The Arctic Research and Policy Act Region—Bering Sea"},{"id":392249,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Pamphlet"},{"id":392268,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3484/coverthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.50390625,\n 69.83962194067463\n ],\n [\n -149.94140625,\n 70.55417853776078\n ],\n [\n -155.91796874999997,\n 71.41317683396566\n ],\n [\n -162.421875,\n 70.19999407534661\n ],\n [\n -166.11328125000003,\n 68.52823492039876\n ],\n [\n -166.640625,\n 67.20403234340081\n ],\n [\n -165.9375,\n 66.93006025862448\n ],\n [\n -168.22265625,\n 65.58572002329473\n ],\n [\n -166.11328125,\n 61.270232790000634\n ],\n [\n -165.58593749999997,\n 60.06484046010452\n ],\n [\n -164.35546875,\n 59.265880628258095\n ],\n [\n -161.3671875,\n 58.81374171570782\n ],\n [\n -147.83203125,\n 65.2198939361321\n ],\n [\n -140.625,\n 65.94647177615738\n ],\n [\n -141.50390625,\n 69.83962194067463\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Regional Director, Alaska
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560
Aquatic systems in and around the Long Island Sound (LIS) provide a variety of ecological and economic benefits, but in some areas of the LIS, aquatic ecosystems have become degraded by excess nitrogen. A substantial fraction of the nitrogen inputs to the LIS are transported through the groundwater-flow system. Because groundwater travel times in surficial aquifers can exceed 100 years, multiyear lags are introduced between inputs at the water table in recharge areas and discharge to inland or coastal receiving waters. The U.S. Geological Survey, in cooperation with the Connecticut Department of Energy and Environmental Protection and the U.S. Environmental Protection Agency’s Long Island Sound Study, developed a steady-state groundwater model of the watersheds draining from the northern shore of the LIS for the purpose of calculating groundwater budgets and travel times to coastal waters.
The model was developed by using the MODFLOW–NWT software and existing spatial data on aquifers, river networks, land-surface altitudes, land cover, groundwater recharge, and water use. Coastal waters were delineated on the basis of the National Wetland Inventory; all non-coastal waters were collectively termed “inland waters.” A coarse-resolution model was calibrated by using the PEST++ software, long-term records of water levels in 65 wells, stream altitudes from 477 streams, base-flow records for 14 streamgages that are relatively unaffected by withdrawals, and error metrics based on incorrectly simulated flooding and incorrectly simulated dry streams. The calibrated values were used in a fine-resolution model in which the mean absolute residuals were 4.5 meters for groundwater levels, 1.3 meters for stream altitudes, and 7,200 cubic meters per day (2.9 cubic feet per second) for base flow. About 89 percent of the terrestrial cells were correctly simulated with the water table below land surface, and nearly 90 percent of the cells representing streams were correctly simulated as having the water table above the stream bottom. Together, these metrics suggest that this model is robust for simulating regional-scale groundwater patterns.
Simulated groundwater budgets were compiled for the entire study area, for each HUC12 (Hydrologic Unit Code no. 12) watershed and its adjacent coastal waters, if applicable, within the study area, and for 14 coastal-embayment watersheds. Most groundwater (90.6 percent of inflows) discharged to inland waters, with smaller fractions to coastal waters (7.0 percent) and well withdrawals (2.4 percent). When computed for HUC12 watersheds with coastal discharge, the portions of groundwater discharging to coastal waters ranged from 0.02 to 66 percent of groundwater outflows, with a median of 13 percent. Within priority-embayment watersheds, the portions of groundwater discharging to coastal waters ranged from 2 to 56 percent, with a median of 15 percent.
Groundwater travel times also were simulated for the entire study area, for each HUC12 watershed and its adjacent coastal waters, if applicable, within the study area and for 14 priority coastal embayments. Within the entire study area, the median groundwater travel time was 1.9 years, with an interquartile range of 0.1 to 5.9 years. Sensitivity analysis of groundwater travel times within a subbasin in the study area indicates that the travel times are a function of the grid resolution, with coarser grids resulting in shorter median travel times. Travel times for groundwater discharging to coastal waters were similar to travel times for groundwater discharging to inland waters, with a median of 1.9 years. Median travel times for the HUC12 watersheds ranged from 0.9 to 53.5 years, with a median of 1.8 years. Among HUC12 watersheds that include coastal areas, travel times for groundwater discharging to coastal waters ranged from less than 1 to 61.6 years, with a median of 2.8 years. The HUC12 watersheds with the longest simulated travel times were in the urban area near New York City where the model performance is less accurate. Median travel times for groundwater discharging to coastal waters within the priority-embayment watersheds ranged from less than 1 to 18.6 years, with a median of 2.3 years.
A more focused analysis was conducted for the Niantic River watershed to demonstrate the applicability of the regional model to local-scale nitrogen-transport analyses by using nitrogen-input and -attenuation rates from literature sources. Nitrogen inputs were estimated by using land-cover-based loading factors, and attenuation was estimated by using attenuation factors based on geologic zones and soil properties. Based on this analysis, groundwater transports an estimated 22,000 kilograms of nitrogen per year (2.9 kilograms of nitrogen per hectare per year) to streams, rivers, and coastal waters within the Niantic River watershed. Approximately 36 percent of discharging nitrogen is from atmospheric-deposition sources, 38 percent is from fertilizers, and 26 percent is from septic systems. Most of the groundwater-transported nitrogen (88 percent) discharges first to streams and rivers, with only 12 percent discharging directly to coastal waters. Travel times for groundwater-transported nitrogen ranged from less than 1 day to more than 100 years, with a median of 1.6 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215116","collaboration":"Prepared in cooperation with the United States Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Barclay, J.R., and Mullaney, J.R., 2021, Simulation of groundwater budgets and travel times for watersheds on the north shore of Long Island Sound, with implications for nitrogen-transport studies: U.S. Geological Survey Scientific Investigations Report 2021–5116, 84 p., https://doi.org/10.3133/sir20215116.","productDescription":"Report: x, 84 p.; 2 Data Releases","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-117840","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":391933,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91TQ895","text":"USGS data release","linkHelpText":"Summary data on groundwater budgets and travel times for watersheds on the north shore of Long Island Sound"},{"id":391932,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BLHPIT","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH groundwater flow models of steady-state conditions in coastal Connecticut and adjacent areas of New York and Rhode Island, as well as a nitrogen transport model of the Niantic River watershed"},{"id":391931,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5116/sir20215116.pdf","text":"Report","size":"30.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5116"},{"id":391930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5116/coverthb.jpg"}],"country":"United States","state":"Connecticut, New York, Rhode Island","otherGeospatial":"Long island Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.9324951171875,\n 40.826280356677124\n ],\n [\n -71.45782470703125,\n 40.826280356677124\n ],\n [\n -71.45782470703125,\n 41.50857729743935\n ],\n [\n -73.9324951171875,\n 41.50857729743935\n ],\n [\n -73.9324951171875,\n 40.826280356677124\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Halogenated organic compounds (HOCs) in marine species collected from the Atlantic Ocean [3 shortfin mako (Isurus oxyrinchus) and 1 porbeagle (Lamna nasus)], and 12 sea turtles collected from the Pacific Ocean [3 loggerhead (Caretta caretta), 3 green (Chelonia mydas), 3 olive ridley (Lepidochelys olivacea), and 3 hawksbill (Eretmochelys imbricata)] were analyzed with a nontargeted analytical method using two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry. Sharks and sea turtles had distinct HOC profiles. Halogenated methoxyphenols (halo-MeOPs) were the most abundant compound class identified in sea turtle livers, while polychlorinated biphenyls (PCBs) were the most abundant in shark livers. In addition to legacy contaminants and halo-MeOPs, a total of 110 nontargeted/novel HOCs (NHOCs) were observed in the shark livers. Shortfin mako collected from the northern Gulf of Mexico contained the largest number (89) and most diverse structural classes of NHOCs. Among all NHOCs, a group of compounds with the elemental composition C14H12–nCln (n = 5–8) exhibited the highest concentrations, followed by chlorocarbazoles and tris(chlorophenyl) methanes (TCPMs). Using nontargeted workflows, a variety of known and unknown HOCs were observed, which demonstrate the need to develop more complete chemical profiles in the marine environment.
Declines of many bumble bee species have raised concerns because of their importance as pollinators and potential harbingers of declines among other insect taxa. At present, bumble bee conservation is predominantly focused on midsummer flower restoration in open habitats. However, a growing body of evidence suggests that forests may play an important role in bumble bee life history. Compared with open habitats, forests and woody edges provide food resources during phenologically distinct periods, are often preferred nesting and overwintering habitats, and can offer favorable abiotic conditions in a changing climate. Future research efforts are needed in order to anticipate how ongoing changes in forests, such as overbrowsing by deer, plant invasions, and shifting canopy demographics, affect the suitability of these habitats for bumble bees. Forested habitats are increasingly appreciated in the life cycles of many bumble bees, and they deserve greater attention from those who wish to understand bumble bee populations and aid in their conservation.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biab121","usgsCitation":"Mola, J.M., Hemberger, J., Kochanski, J., Richardson, L.L., and Pearse, I., 2021, The importance of forests in bumble bee biology and conservation: BioScience, v. 71, no. 21, p. 1234-1248, https://doi.org/10.1093/biosci/biab121.","productDescription":"15 p.","startPage":"1234","endPage":"1248","ipdsId":"IP-132663","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450122,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biab121","text":"Publisher Index Page"},{"id":393094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"21","noUsgsAuthors":false,"publicationDate":"2021-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Mola, John Michael 0000-0002-5394-9071","orcid":"https://orcid.org/0000-0002-5394-9071","contributorId":224281,"corporation":false,"usgs":true,"family":"Mola","given":"John","email":"","middleInitial":"Michael","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":828642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hemberger, Jeremy 0000-0003-3648-4724","orcid":"https://orcid.org/0000-0003-3648-4724","contributorId":270192,"corporation":false,"usgs":false,"family":"Hemberger","given":"Jeremy","email":"","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":828643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kochanski, Jade 0000-0001-8693-2404","orcid":"https://orcid.org/0000-0001-8693-2404","contributorId":270193,"corporation":false,"usgs":false,"family":"Kochanski","given":"Jade","email":"","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":828644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richardson, Leif L. 0000-0003-4855-5737","orcid":"https://orcid.org/0000-0003-4855-5737","contributorId":270194,"corporation":false,"usgs":false,"family":"Richardson","given":"Leif","email":"","middleInitial":"L.","affiliations":[{"id":34267,"text":"The Xerces Society for Invertebrate Conservation","active":true,"usgs":false}],"preferred":false,"id":828645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":211154,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":828646,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238169,"text":"70238169 - 2021 - Resource use among top-level piscivores in a temperate reservoir: Implications for a threatened coldwater specialist","interactions":[],"lastModifiedDate":"2022-11-15T12:41:50.325552","indexId":"70238169","displayToPublicDate":"2021-11-30T06:39:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Resource use among top-level piscivores in a temperate reservoir: Implications for a threatened coldwater specialist","docAbstract":"Evaluations of resource use among native piscivores in natural lakes have consistently documented significant partitioning that supports coexistence. Partitioning may be less prominent in reservoirs where water-level fluctuations can compress habitat and trophic diversity, but studies are lacking. Stable isotopes and bioenergetic models were used to quantify trophic interactions within a native piscivore assemblage inhabiting a temperate irrigation reservoir and explore implications for coexistence with a focus on threatened bull trout (Salvelinus confluentus). As hypothesised, adult bull trout exhibited the greatest degree of trophic specialisation by consuming mostly coldwater pelagic forage fish, which were consumed seasonally by the more abundant burbot (Lota lota) and northern pikeminnow (Ptychocheilus oregonensis). Numerous trophic niche overlap probabilities exceeded 70%, were as high as 93% and greatest between bull trout and burbot. Bioenergetics simulations demonstrated the high seasonal consumption capacity of burbot relative to northern pikeminnow. As a result, threefold to fourfold fewer burbot were required to consume the annual productivity of coldwater prey important for bull trout, particularly in the absence of small-bodied mesothermic or eurythermal fish as a buffer. Collectively, our analysis elucidated relatively strong trophic niche overlap among similarly sized piscivores, the importance of maintaining a diverse forage fish community for promoting coexistence and the greatest potential for competitive interactions between adult bull trout and burbot if key prey were limited or less diverse. More studies in regulated systems are needed to test for consistent patterns and identify mechanisms that limit or promote coexistence amid growing human-induced environmental change and demands on freshwater.
Managers of our Nation’s resources face unprecedented challenges driven by the convergence of increasing, competing societal demands and a changing climate that affects the stability, vulnerability, and predictability of those resources. To help meet these challenges, the scientific community must take advantage of all available technologies, data, and integrative Earth systems modeling capacity to better inform resource and risk management decisions. This is the overarching goal of the U.S. Geological Survey (USGS) Earth Monitoring, Analysis, and Prediction (EarthMAP) vision: “By 2030, the USGS will deliver well integrated observations and predictions of the future state of natural systems—water, ecosystems, energy, minerals, hazards—at regional and national scales, working primarily with federal, state, and academic partners to develop and operate the capability” (U.S. Geological Survey, 2021).
Providing more integrated Earth systems science and actionable information to decision makers, stakeholders, and the public requires a better understanding of the depth and distribution of existing capacity (capabilities, tools, and techniques) across the Bureau. Identifying existing capacity is also a critical first step toward gap analysis and targeted investments to increase capacity over time. The USGS formed a Capacity Assessment Team (CAT) and charged it with (1) conducting a Request for Information (RFI) to identify existing USGS expertise and activities supportive of integrated and predictive science to inform decision making, (2) developing a strategy and proof-of-concept for a continuously updated capacity assessment capability, and (3) identifying lessons learned to inform development of best practices for future capacity assessment efforts.
The RFI took the form of a survey, with content guided by the science and technology needs identified in a USGS report titled “Grand Challenges for Integrated U.S. Geological Survey Science—A Workshop Report” (Jenni and others, 2017). The 44-question survey provided respondents the ability to rate their level of experience with a suite of priority disciplines, analysis and modeling approaches, technologies, and stakeholder engagement strategies and to enter optional narrative text for supporting context. An introductory portion focused on general science capacity assessment, followed by three sections targeting capabilities related to the foundational components of EarthMAP: (1) data and information integration, (2) integrated predictive science, and (3) actionable information.
The survey results provided a high-level snapshot of USGS capacity in the targeted areas. Respondents (1,035 individuals) represented approximately 13 percent of the USGS across all mission areas and regions. Seventy-four percent of the respondents held a science-focused position title and the remainder had position titles in information technology, computer science, management, administrative, or other (contractors, volunteers, emeritus, and unknown). To provide greater insight into respondent capabilities and activities, information from the U.S. Department of the Interior and USGS enterprise information systems were used to further characterize topical expertise and organizational associations of survey respondents. To address the ongoing need to assess the Bureau’s capacity to address integrated predictive science priorities, the CAT developed a software-based proof-of-concept called the Integrated Science Assessment Information Database (iSAID) for assembling various information sources together toward making the full extent of USGS capabilities and scientific assets available for routine capacity assessment. This proof-of-concept is intended to serve as a catalyst for further development. The process of implementing the EarthMAP capacity assessment survey, analyzing survey responses, and developing the proof-of-concept resulted in lessons learned, findings, and recommendations. Example scenarios throughout the report demonstrate how capacity assessment data can inform science planning. Three overarching findings and recommendations are:
(1) Finding: Capacity is limited in some critical disciplines, skills, and technology applications, but “sufficient” depends on the question and the need relative to availability at a given point in time.
Recommendation: Develop an on-demand capacity assessment framework that enables rapid identification and evaluation of existing and available expertise to support decision needs as they arise.
(2) Finding: Institutional barriers and lack of awareness constrain the ability of USGS staff to adopt new technologies, collaborate across administrative boundaries, and deliver actionable information to stakeholders in a timely manner. However, these barriers are not universally experienced.
Recommendation: Pursue more targeted inquiries to clarify which institutional barriers are obstructing the adoption of new technologies and approaches or the sharing of expertise and equipment across organizational and regional boundaries. These inquiries should inform USGS leadership, mission areas, and regions whether policies can be revised or whether a lack of understanding is creating perceived obstacles. Highlight cases when staff have successfully adopted new technologies and approaches to advance EarthMAP priorities and provide actionable information in a timely manner to spread awareness of how perceived obstacles can be navigated and overcome when appropriate.
(3) Finding: Examples of people and projects integrating across disciplines and scales and applying advanced approaches to meet complex stakeholder needs exist. Such examples provide transfer value across the spectrum from approach to decision making. Many projects, already underway, appear to meet elements of the EarthMAP vision, and the USGS has people who can provide leadership in multiple types of specific integrated science efforts.
Recommendation: Use these findings as a starting point for near-term strategic planning for integrated science. Highlight, incentivize, and build on existing interdisciplinary predictive science and information delivery activities across the USGS to advance toward further realization of an EarthMAP capacity.
The CAT efforts to develop and assess existing USGS capacity to advance the EarthMAP vision revealed a fundamental challenge for not only this effort but any effort to assess existing capacity: A considerable amount of thought, time, and effort is required to survey and assess capabilities and tools available to support a given need, yet best results are still likely to provide an incomplete assessment. To better meet the frequent need to assess capabilities, tools, products, and projects that address an expressed strategic priority, the CAT proposes the concept of an on-demand capacity assessment framework supported by a software package that dynamically pulls and integrates information from existing USGS information systems and public domain registries. Although existing USGS enterprise information systems currently lack the structure, cross-system consistency, interoperability, and stability to support a continuously updated capacity assessment capability, we identify reasonable near-term steps to improve the utility of information gathered on expertise and project capacity and to improve the consistency and completeness of information and the ability of USGS systems to share that information. The ability to search and characterize this information will make future assessments of capacity faster, more complete, more efficient, and more targeted. This approach would grow the Bureau’s capacity knowledge over time, iteratively improving the ability to access, leverage, and synthesize existing capabilities and assets as well as identify and fill critical gaps. The greatest promise for developing integrated science could lie in linking across existing projects and expertise to create a multi-project capacity for addressing large, complex environmental issues.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211102","usgsCitation":"Keisman, J.L., Bristol, S., Brown, D.S., Flickinger, A.K., Gunther, G., Murdoch, P.S., Musgrove, M., Nelson, J.C., Steyer, G.D., Thomas, K.A., and Waite, I.R., 2021, Capacity assessment for Earth Monitoring, Analysis, and Prediction (EarthMAP) and future integrated monitoring and predictive science at the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2021-1102, 110 p., https://doi.org/10.3133/ofr20211102.","productDescription":"Report: v, 110 p.; Data Release","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-129970","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":392008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BB5NMZ","linkHelpText":"USGS Earthmap Capacity Assessment Dataset"},{"id":392006,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1102/images"},{"id":392005,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1102/ofr20211102.xml"},{"id":392004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1102/ofr20211102.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":392003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1102/covrthb.jpg"}],"contact":"Director,
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The widespread application of directional drilling and hydraulic fracturing technologies expanded oil and gas (OG) development to previously inaccessible resources. A single OG well can generate millions of liters of wastewater, which is a mixture of brine produced from the fractured formations and injected hydraulic fracturing fluids (HFFs). With thousands of wells completed each year, safe management of OG wastewaters has become a major challenge to the industry and regulators. OG wastewaters are commonly disposed of by underground injection, and previous research showed that surface activities at an Underground Injection Control (UIC) facility in West Virginia affected stream biogeochemistry and sediment microbial communities immediately downstream from the facility. Because microbially driven processes can control the fate and transport of organic and inorganic components of OG wastewater, we designed a series of aerobic microcosm experiments to assess the influence of high total dissolved solids (TDS) and two common HFF additives—the biocide 2,2-dibromo-3-nitrilopropionamide (DBNPA) and ethylene glycol (an anti-scaling additive)—on microbial community structure and function. Microcosms were constructed with sediment collected upstream (background) or downstream (impacted) from the UIC facility in West Virginia. Exposure to elevated TDS resulted in a significant decrease in aerobic respiration, and microbial community analysis following incubation indicated that elevated TDS could be linked to the majority of change in community structure. Over the course of the incubation, the sediment layer in the microcosms became anoxic, and addition of DBNPA was observed to inhibit iron reduction. In general, disruptions to microbial community structure and function were more pronounced in upstream and background sediment microcosms than in impacted sediment microcosms. These results suggest that the microbial community in impacted sediments had adapted following exposure to OG wastewater releases from the site. Our findings demonstrate the potential for releases from an OG wastewater disposal facility to alter microbial communities and biogeochemical processes. We anticipate that these studies will aid in the development of useful models for the potential impact of UIC disposal facilities on adjoining surface water and shallow groundwater.
No abstract available.
","language":"English","publisher":"LiDAR","usgsCitation":"Stoker, J.M., Sampath, A., Kim, M., Irwin, J., Rounds, E., Heyer, J., Davenport, J., Bellante, G., Kimmet, T., McCormick, C., and Mootz, J., 2021, Airborne hybrid sensor maps the country: Multi-agency effort for testing a potential new hybrid 3DEP-NAIP sensor: LiDAR Magazine, v. 11, no. 4, p. 6-16.","productDescription":"11 p.","startPage":"6","endPage":"16","ipdsId":"IP-122873","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":392783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":392774,"type":{"id":15,"text":"Index Page"},"url":"https://lidarmag.com/wp-content/uploads/emag/2021/vol11no4/index.html"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.94091796875,\n 39.690280594818034\n ],\n [\n -105.79833984375,\n 39.690280594818034\n ],\n [\n -105.79833984375,\n 40.329795743702064\n ],\n [\n -106.94091796875,\n 40.329795743702064\n ],\n [\n -106.94091796875,\n 39.690280594818034\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.2490234375,\n 40.027614437486655\n ],\n [\n -104.67773437499999,\n 40.027614437486655\n ],\n [\n -104.67773437499999,\n 40.51379915504413\n ],\n [\n -105.2490234375,\n 40.51379915504413\n ],\n [\n -105.2490234375,\n 40.027614437486655\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":828217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sampath, Aparajithan 0000-0002-6922-4913 asampath@usgs.gov","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":3622,"corporation":false,"usgs":true,"family":"Sampath","given":"Aparajithan","email":"asampath@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":828218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kim, Minsu 0000-0003-4472-0926 minsukim@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":216429,"corporation":false,"usgs":true,"family":"Kim","given":"Minsu","email":"minsukim@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":828219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Jeffrey 0000-0001-5828-0787 jrirwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5828-0787","contributorId":222485,"corporation":false,"usgs":true,"family":"Irwin","given":"Jeffrey","email":"jrirwin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":828220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rounds, Eric","contributorId":269997,"corporation":false,"usgs":false,"family":"Rounds","given":"Eric","email":"","affiliations":[],"preferred":false,"id":828259,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heyer, Josh","contributorId":269998,"corporation":false,"usgs":false,"family":"Heyer","given":"Josh","email":"","affiliations":[],"preferred":false,"id":828260,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davenport, Julie","contributorId":269999,"corporation":false,"usgs":false,"family":"Davenport","given":"Julie","email":"","affiliations":[],"preferred":false,"id":828261,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bellante, Gabe","contributorId":270000,"corporation":false,"usgs":false,"family":"Bellante","given":"Gabe","email":"","affiliations":[],"preferred":false,"id":828262,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kimmet, Tony","contributorId":270001,"corporation":false,"usgs":false,"family":"Kimmet","given":"Tony","email":"","affiliations":[],"preferred":false,"id":828263,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McCormick, Collin","contributorId":270002,"corporation":false,"usgs":false,"family":"McCormick","given":"Collin","email":"","affiliations":[],"preferred":false,"id":828264,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mootz, John","contributorId":270003,"corporation":false,"usgs":false,"family":"Mootz","given":"John","email":"","affiliations":[],"preferred":false,"id":828265,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70231262,"text":"70231262 - 2021 - Permafrost characterization and feature identification using public domain airborne electromagnetic data, interior Alaska","interactions":[],"lastModifiedDate":"2022-05-04T14:39:00.272796","indexId":"70231262","displayToPublicDate":"2021-11-26T09:09:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7446,"text":"FastTIMES","active":true,"publicationSubtype":{"id":10}},"title":"Permafrost characterization and feature identification using public domain airborne electromagnetic data, interior Alaska","docAbstract":"The Alaska Division of Geological & Geophysical Surveys (DGGS) airborne electromagnetic (AEM) data are an excellent resource for permafrost characterization. AEM data can be used for pingo identification, estimating permafrost thickness, estimating surface talik thickness, evaluating permafrost health (temperature), talik identification and more. Data examples are shown from discontinuous permafrost areas just north of Fairbanks, Alaska, USA. Interpretations are made from 2D and 3D resistivity models created from 1D inversions of the Goldstream Valley AEM survey data (Emond, 2018a).","language":"English","publisher":"Environmental and Engineering Geophysical Society","usgsCitation":"Emond, A.M., Daanen, R., and Minsley, B.J., 2021, Permafrost characterization and feature identification using public domain airborne electromagnetic data, interior Alaska: FastTIMES, v. 26, no. 3.","ipdsId":"IP-133148","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":400130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":400119,"type":{"id":15,"text":"Index Page"},"url":"https://fasttimesonline.co/permafrost-characterization-and-feature-identification-using-public-domain-airborne-electromagnetic-data-interior-alaska/"}],"country":"United States","state":"Alaska","otherGeospatial":"Goldstream Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.1561279296875,\n 64.7846582967133\n ],\n [\n -147.271728515625,\n 64.7846582967133\n ],\n [\n -147.271728515625,\n 65.23255403681249\n ],\n [\n -148.1561279296875,\n 65.23255403681249\n ],\n [\n -148.1561279296875,\n 64.7846582967133\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Emond, Abraham M.","contributorId":216313,"corporation":false,"usgs":false,"family":"Emond","given":"Abraham","email":"","middleInitial":"M.","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":842154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Daanen, Ronald","contributorId":191060,"corporation":false,"usgs":false,"family":"Daanen","given":"Ronald","email":"","affiliations":[],"preferred":false,"id":842155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":842156,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225689,"text":"70225689 - 2021 - Random forest","interactions":[],"lastModifiedDate":"2021-11-03T13:15:33.168421","indexId":"70225689","displayToPublicDate":"2021-11-26T08:13:03","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Random forest","docAbstract":"This entry defines and discusses the random forest machine learning algorithm. The algorithm is used to predict class or quantities for target variables using values of a set of predictor variables. It uses decision trees that are generated from bootstrap sampling of the training data set to create a \"forest\". The entry discusses the algorithm steps, the interpretative tools of the resulting model, current areas of research, and its limitations. Applications to the quantitative geosciences are reviewed as well as availability of software to implement the algorithm.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of mathematical geosciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Link","doi":"10.1007/978-3-030-26050-7_265-1","usgsCitation":"Attanasi, E., and Coburn, T., 2021, Random forest, chap. of Encyclopedia of mathematical geosciences, HTML Document, https://doi.org/10.1007/978-3-030-26050-7_265-1.","productDescription":"HTML Document","ipdsId":"IP-123941","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":391316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":826267,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coburn, Timothy","contributorId":245358,"corporation":false,"usgs":false,"family":"Coburn","given":"Timothy","affiliations":[],"preferred":false,"id":826268,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229729,"text":"70229729 - 2021 - Can identifying discrete behavioral groups with individual-based acoustic telemetry advance the understanding of fish distribution patterns?","interactions":[],"lastModifiedDate":"2022-03-16T16:30:29.343798","indexId":"70229729","displayToPublicDate":"2021-11-25T11:24:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Can identifying discrete behavioral groups with individual-based acoustic telemetry advance the understanding of fish distribution patterns?","docAbstract":"Identifying patterns of organismal distribution can provide valuable insights for basic and applied marine and coastal ecology because understanding where animals are located is foundational to both research and science-based conservation. Understanding variation in distributional patterns can lead to a better assessment of ecological drivers and an improved ability to predict consequences of natural and altered relationships. Here, our purpose is to explore if quantifying coexisting groups of individual fish predators advances our understanding of field distribution patterns. Toward this end, we quantified locations of 59 acoustically tagged striped bass (Morone saxatilis) within a 26-stationary unit telemetry receiver array in Plum Island Estuary (PIE), MA, United States. We then used cluster analyses on spatial and temporal-spatial metrics from this dataset to (1) assess if distinct groups of individuals coexisted, (2) quantify group characteristics, and (3) test associations between groups and distribution (e.g., physical site type and region). Based on multiple lines of evidence, we identified four groups of striped bass with different space use patterns that persisted across seasons (summer and fall). Similar-sized striped bass clustered at spatial and temporal scales at which individuals within distinct groups could, and did, physically overlap. In addition, distributional groups were linked to components of physical site type and region suggesting that discrete groups of individuals can interact differently with the environment within the same ecological system. The identification of these distinct groups of individuals creates a baseline from which to explore further ecological implications of grouping behavior for research and conservation in geographically large, temporally dynamic, and spatially heterogeneous marine and coastal environments.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.723025","usgsCitation":"Taylor, R.B., Mather, M.E., Smith, J., and Boles, K., 2021, Can identifying discrete behavioral groups with individual-based acoustic telemetry advance the understanding of fish distribution patterns?: Frontiers in Marine Science, v. 8, 712025, 12 p., https://doi.org/10.3389/fmars.2021.723025.","productDescription":"712025, 12 p.","ipdsId":"IP-129993","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450128,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2021.723025","text":"Publisher Index Page"},{"id":397177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Plum Island Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.89237213134766,\n 42.68344492725023\n ],\n [\n -70.74199676513672,\n 42.68344492725023\n ],\n [\n -70.74199676513672,\n 42.79313328756228\n ],\n [\n -70.89237213134766,\n 42.79313328756228\n ],\n [\n -70.89237213134766,\n 42.68344492725023\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2021-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Ryland B.","contributorId":288583,"corporation":false,"usgs":false,"family":"Taylor","given":"Ryland","email":"","middleInitial":"B.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":838118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mather, Martha E. 0000-0003-3027-0215 mather@usgs.gov","orcid":"https://orcid.org/0000-0003-3027-0215","contributorId":2580,"corporation":false,"usgs":true,"family":"Mather","given":"Martha","email":"mather@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Joseph M.","contributorId":288584,"corporation":false,"usgs":false,"family":"Smith","given":"Joseph M.","affiliations":[{"id":61805,"text":"Northwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":838119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boles, Kayla M.","contributorId":288585,"corporation":false,"usgs":false,"family":"Boles","given":"Kayla M.","affiliations":[{"id":13409,"text":"Kentucky Department of Fish & Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":838120,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225606,"text":"70225606 - 2021 - Warming sea surface temperatures fuel summer epidemics of eelgrass wasting disease","interactions":[],"lastModifiedDate":"2022-01-12T16:33:48.759457","indexId":"70225606","displayToPublicDate":"2021-11-25T10:19:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Warming sea surface temperatures fuel summer epidemics of eelgrass wasting disease","docAbstract":"Seawater temperatures are increasing, with many unquantified impacts on marine diseases. While prolonged temperature stress can accelerate host-pathogen interactions, the outcomes in nature are poorly quantified. We monitored eelgrass wasting disease (EWD) from 2013-2017 and correlated mid-summer prevalence of EWD with remotely sensed seawater temperature metrics before, during, and after the 2015-2016 marine heatwave in the northeast Pacific, the longest marine heatwave in recent history. Eelgrass shoot density declined by 60% between 2013 and 2015 and did not recover. EWD prevalence ranged from 5-70% in 2013 and increased to 60-90% by 2017. EWD severity approximately doubled each year between 2015 and 2017. EWD prevalence was positively correlated with warmer temperature for the month prior to sampling while EWD severity was negatively correlated with warming prior to sampling. This complex result may be mediated by leaf growth; bigger leaves may be more likely to be diseased, but may also grow faster than lesions, resulting in lower severity. Regional stressors leading to population declines prior to or early in the heatwave may have exacerbated the effects of warming on eelgrass disease susceptibility and reduced the resilience of this critical species.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13902","usgsCitation":"Groner, M., Eisenlord, M.E., Yoshioka, R.M., Fiorenza, E.A., Dawkins, P.D., Graham, O.J., Winningham, M., Vompe, A., Rivlin, N.D., Yang, B., Burge, C.A., Rappazzo, B., Gomes, C.P., and Harvell, C.D., 2021, Warming sea surface temperatures fuel summer epidemics of eelgrass wasting disease: Marine Ecology Progress Series, v. 679, p. 47-58, https://doi.org/10.3354/meps13902.","productDescription":"12 p.","startPage":"47","endPage":"58","ipdsId":"IP-128676","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":394248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"679","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Groner, Maya 0000-0002-3381-6415","orcid":"https://orcid.org/0000-0002-3381-6415","contributorId":220169,"corporation":false,"usgs":true,"family":"Groner","given":"Maya","email":"","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":825860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eisenlord, Morgan E.","contributorId":139304,"corporation":false,"usgs":false,"family":"Eisenlord","given":"Morgan","email":"","middleInitial":"E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":825861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yoshioka, Reyn M.","contributorId":268118,"corporation":false,"usgs":false,"family":"Yoshioka","given":"Reyn","email":"","middleInitial":"M.","affiliations":[{"id":55556,"text":"Oregon Institute of Marine Biology, University of Oregon, Charleston, OR 97420, USA","active":true,"usgs":false}],"preferred":false,"id":825862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fiorenza, Evan A.","contributorId":268119,"corporation":false,"usgs":false,"family":"Fiorenza","given":"Evan","email":"","middleInitial":"A.","affiliations":[{"id":55557,"text":"Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA 92697, USA","active":true,"usgs":false}],"preferred":false,"id":825863,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dawkins, Phoebe D.","contributorId":268120,"corporation":false,"usgs":false,"family":"Dawkins","given":"Phoebe","email":"","middleInitial":"D.","affiliations":[{"id":55557,"text":"Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA 92697, USA","active":true,"usgs":false}],"preferred":false,"id":825864,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graham, Olivia J.","contributorId":268121,"corporation":false,"usgs":false,"family":"Graham","given":"Olivia","email":"","middleInitial":"J.","affiliations":[{"id":55558,"text":"Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY 14853, USA","active":true,"usgs":false}],"preferred":false,"id":825865,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Winningham, Miranda","contributorId":268122,"corporation":false,"usgs":false,"family":"Winningham","given":"Miranda","email":"","affiliations":[{"id":55557,"text":"Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA 92697, USA","active":true,"usgs":false}],"preferred":false,"id":825866,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vompe, Alex","contributorId":268123,"corporation":false,"usgs":false,"family":"Vompe","given":"Alex","email":"","affiliations":[{"id":55560,"text":"Department of Microbiology, Oregon State University, Corvallis, OR 97331, USA","active":true,"usgs":false}],"preferred":false,"id":825867,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rivlin, Natalie D.","contributorId":268124,"corporation":false,"usgs":false,"family":"Rivlin","given":"Natalie","email":"","middleInitial":"D.","affiliations":[{"id":55561,"text":"University of British Columbia, Vancouver, BC V6T 1Z1, Canada","active":true,"usgs":false}],"preferred":false,"id":825868,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yang, Bo","contributorId":149369,"corporation":false,"usgs":false,"family":"Yang","given":"Bo","email":"","affiliations":[{"id":13653,"text":"University South Florida","active":true,"usgs":false}],"preferred":false,"id":825869,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Burge, Colleen A.","contributorId":213539,"corporation":false,"usgs":false,"family":"Burge","given":"Colleen","email":"","middleInitial":"A.","affiliations":[{"id":38781,"text":"Institute of Marine and Environmental Technology, University of Maryland Baltimore County, Baltimore, MD 21202","active":true,"usgs":false}],"preferred":false,"id":825870,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rappazzo, Brendan","contributorId":268125,"corporation":false,"usgs":false,"family":"Rappazzo","given":"Brendan","email":"","affiliations":[{"id":55562,"text":"Department of Computer Science, Cornell University, Ithaca, NY 14853, USA","active":true,"usgs":false}],"preferred":false,"id":825871,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Gomes, Carla P.","contributorId":177112,"corporation":false,"usgs":false,"family":"Gomes","given":"Carla","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":825872,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Harvell, C. Drew","contributorId":32843,"corporation":false,"usgs":false,"family":"Harvell","given":"C.","email":"","middleInitial":"Drew","affiliations":[],"preferred":false,"id":825873,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70227039,"text":"70227039 - 2021 - The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States","interactions":[],"lastModifiedDate":"2021-12-28T15:34:19.88258","indexId":"70227039","displayToPublicDate":"2021-11-25T09:32:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States","docAbstract":"Juvenile tree survival will play an important role in the persistence of coniferous forests and woodlands in the southwestern United States (SWUS). Vulnerability to climatic and environmental stress declines as trees grow, such that larger, more deeply rooted juveniles are less likely to experience mortality. It is unclear how juvenile conifers partition the aboveground and belowground components of early growth, if growth differs between species and ecosystem types, and what environmental factors influence juvenile carbon allocation above- or belowground. We developed a novel data set for four juvenile conifer groups (junipers, piñon pines, ponderosa pines, firs; 1121 juveniles sampled, 221 destructively) in three height classes (<150 mm, 150–300 mm, and 300+ mm), across 25 SWUS sites. We compared growth characteristics across groups and height classes and related differences to climatic and environmental factors. As tree height increased from <150 mm to 300+ mm, belowground growth increased, root:shoot ratio declined, and specific leaf area declined for all conifers except firs. Maximum rooting depth was shallower than previous estimates (<˜400 mm). Lower elevation juveniles were frequently located in sheltered microsites that provided high shading, whereas mid- and higher elevation juveniles were frequently unsheltered. Across all forest and woodland sites, herbaceous cover was positively correlated with aboveground growth. At study locations comprised of multiple sites, differences in aboveground growth were best explained by ecosystem type (piñon pine-juniper woodland, ponderosa pine forest, mixed-conifer forest) and local environmental variation. Our results indicate generally more belowground early growth and more aboveground later growth, but specific allocation patterns varied among ecosystem (greater proportional shoot growth at lower and mid-elevations compared with higher elevations). Juvenile conifers had similar magnitudes of proportional growth across conifer groups, displaying limited capacity to acclimate growth to differences in climate that control ecosystem type. If juvenile conifers also do not acclimate physiologically to their environment, our findings suggest that local environmental variation will play a primary role in regulating forest and woodland persistence and modify the effects of climate change in the SWUS.
Reproducing populations of invasive carps (Hypophthalmichthys spp.) could alter aquatic food webs and negatively affect native fishes in the Mississippi National River and Recreation Area (MISS) and the St. Croix National Scenic Riverway (SACN). However, proposed invasive carp barriers may also threaten populations of native migratory fishes by preventing movements of fish between rivers that are necessary for life history requirements. In this study, nonlethal chemical techniques were used to provide baseline data related to the condition, trophic position, and migratory histories of lake sturgeon (Acipenser fulvescens) captured in the Mississippi and St. Croix Rivers.
Fish length and weight measurements and age estimates determined from pectoral fin rays demonstrated that lake sturgeon from the Mississippi River had greater lengths-at-age compared to sturgeon from the St. Croix River. However, length–weight relations were similar for sturgeon from the Mississippi and St. Croix Rivers. Lake sturgeon captured from different locations had distinguishable fatty acid signatures, and stable isotope analyses demonstrated that lake sturgeon from the Mississippi River generally feed at a higher trophic level than those in the St. Croix River. Strontium-to-calcium ratios (Sr:Ca) from fin ray cross sections indicated that sturgeon captured from the Mississippi River had higher Sr:Ca values than sturgeon captured from the St. Croix River, and natal origins and capture locations were not significantly different among sturgeon captured within individual rivers. Most sturgeon were captured in water with a similar Sr:Ca signature as their natal waters, indicating that there is some separation between populations of lake sturgeon in the St. Croix and Mississippi Rivers. However, Sr:Ca data indicated substantial variation in movement patterns among individual lake sturgeon, indicating that populations interact through migrations of individual fish between rivers.
Study results provide baseline condition and food web structure index data for assessing changes in lake sturgeon populations should invasive carps become established in these areas of the Mississippi and St. Croix Rivers. Controlled-exposure and telemetry studies would help verify and enhance the relations between Sr:Ca signatures in water and lake sturgeon pectoral fin rays to further assess mixing of sturgeons between rivers.
","language":"English","publisher":"Springer","doi":"10.1186/s13717-021-00344-y","usgsCitation":"Ziegeweid, J.R., Bartsch, M., Bartsch, L., Zigler, S., Kennedy, R., and Love, S.A., 2021, Assessing the migratory histories, trophic positions, and conditions of lake sturgeon in the St. Croix and Mississippi Rivers using fin ray microchemistry, stable isotopes, and fatty acid profiles: Ecological Processes, v. 10, 72, 22 p., https://doi.org/10.1186/s13717-021-00344-y.","productDescription":"72, 22 p.","ipdsId":"IP-131342","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":450133,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13717-021-00344-y","text":"Publisher Index Page"},{"id":394505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Mississippi River, St. Croix River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.22998046875,\n 44.64911632343077\n ],\n [\n -92.51312255859375,\n 44.64911632343077\n ],\n [\n -92.51312255859375,\n 45.31159750379206\n ],\n [\n -93.22998046875,\n 45.31159750379206\n ],\n [\n -93.22998046875,\n 44.64911632343077\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2021-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ziegeweid, Jeffrey R. 0000-0001-7797-3044 jrziege@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-3044","contributorId":4166,"corporation":false,"usgs":true,"family":"Ziegeweid","given":"Jeffrey","email":"jrziege@usgs.gov","middleInitial":"R.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartsch, Michelle 0000-0002-9571-5564 mbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-9571-5564","contributorId":3165,"corporation":false,"usgs":true,"family":"Bartsch","given":"Michelle","email":"mbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":831107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":831108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zigler, Steven J. 0000-0002-4153-0652","orcid":"https://orcid.org/0000-0002-4153-0652","contributorId":244025,"corporation":false,"usgs":false,"family":"Zigler","given":"Steven J.","affiliations":[{"id":48800,"text":"Former USGS, UMESC employee","active":true,"usgs":false}],"preferred":false,"id":831109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kennedy, Robert J 0000-0003-2135-5022","orcid":"https://orcid.org/0000-0003-2135-5022","contributorId":215686,"corporation":false,"usgs":false,"family":"Kennedy","given":"Robert J","affiliations":[{"id":39305,"text":"Former UMESC employee - retired","active":true,"usgs":false}],"preferred":false,"id":831110,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Love, Seth A.","contributorId":209950,"corporation":false,"usgs":false,"family":"Love","given":"Seth","email":"","middleInitial":"A.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":831111,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226743,"text":"70226743 - 2021 - Long-term Pseudogymnoascus destructans surveillance data reveal factors contributing to pathogen presence","interactions":[],"lastModifiedDate":"2023-06-23T13:15:26.338878","indexId":"70226743","displayToPublicDate":"2021-11-25T06:49:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Long-term Pseudogymnoascus destructans surveillance data reveal factors contributing to pathogen presence","docAbstract":"The disease white-nose syndrome (WNS) was first recognized in upstate New York in 2006 and has since spread across much of the United States (U.S.), causing severe mortality in several North American bat species. To aid in the identification and monitoring of at-risk bat populations, we evaluate factors associated with the presence of the causative fungal agent of WNS, Pseudogymnoascus destructans (Pd), in the continental United States. We obtained Pd samples through hibernaculum surveys conducted from 2013 to 2020, with all samples analyzed at the U.S. Geological Survey National Wildlife Health Center. Using generalized additive models, we estimated the likelihood of Pd presence under three different hypotheses: human-mediated, species-mediated, and hibernaculum type. In addition to hypothesis-related predictor variables, a subset of models included a smoothed nonseparable effect of longitude and latitude and a smoothed effect of time since study onset to account for spatial and temporal autocorrelation. Under all hypotheses, models indicated probability of Pd detection is best described by the smoothed nonseparable effect of longitude and latitude and a smoothed effect of time since onset of this study. After accounting for spatial and temporal autocorrelations, only hibernaculum type significantly affected Pd presence, with mines and culverts/tunnels less likely to contain Pd compared with caves. Reduced likelihood of Pd presence in mines and culverts/tunnels bodes well for bats of the western and southern United States, where use of these hibernaculum types is more common. While our findings can help guide monitoring and management efforts, the potential for long-distance dispersal combined with variation in community composition and hibernation ecology between the western and eastern United States necessitates the continued monitoring of Pd presence.
The U.S. Geological Survey (USGS) Water Resources Research Act (WRRA) Program connects our Nation’s academic capital to the USGS mission by delivering university-based research, outreach, and education services to our citizens. For more than 50 years, the WRRA Program has invested in local, State, and regionally focused water-related research; information and technology transfer; and workforce development through student training and professional internships. This report presents a long-term (10-year) vision for the WRRA Program that the USGS Water Resources Mission Area and 54 State water resources research institutes (or centers) can use to continue this Federal-State partnership to help solve the Nation’s water problems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1488","usgsCitation":"Donohue, M.J., Greene, E.A., and Lerner, D.T., 2021, Water Resources Research Act Program—Current status, development opportunities, and priorities for 2020–30: U.S. Geological Survey Circular 1488, 27 p., https://doi.org/10.3133/cir1488.","productDescription":"vii, 27 p.","numberOfPages":"40","onlineOnly":"N","ipdsId":"IP-120216","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":392091,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1488/images"},{"id":392090,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1488/cir1488.XML","size":"139 kB","linkFileType":{"id":8,"text":"xml"},"description":"Circular 1488"},{"id":392089,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1488/cir1488.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1488"},{"id":392088,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1488/coverthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.67138671875,\n 54.686534234529695\n ],\n [\n -129.9462890625,\n 55.36662484928637\n ],\n [\n -130.1220703125,\n 56.145549500679074\n ],\n [\n -131.9677734375,\n 56.9449741808516\n ],\n [\n -135.3076171875,\n 59.833775202184206\n ],\n [\n -136.38427734375,\n 59.65664225341022\n ],\n [\n -136.6259765625,\n 59.23217626921806\n ],\n [\n -137.52685546875,\n 58.938673187948304\n ],\n [\n -137.65869140625,\n 59.33318942659219\n ],\n [\n -138.8232421875,\n 60.009970961180386\n ],\n [\n -139.21874999999997,\n 60.108670463036\n ],\n [\n -139.04296875,\n 60.403001945865476\n ],\n [\n -139.85595703125,\n 60.337823495982015\n ],\n [\n -140.99853515625,\n 60.337823495982015\n ],\n [\n -141.15234374999997,\n 69.71810669906763\n ],\n [\n -143.4375,\n 70.17020068549206\n ],\n [\n -145.1953125,\n 70.08056215839737\n ],\n [\n -149.765625,\n 70.58341752317065\n ],\n [\n -152.40234375,\n 70.61261423801925\n ],\n [\n -152.314453125,\n 70.95969716686398\n ],\n [\n -157.1484375,\n 71.35706654962706\n ],\n [\n -159.9609375,\n 70.8734913192635\n ],\n [\n -162.0703125,\n 70.31873847853124\n ],\n [\n -163.916015625,\n 69.06856318696033\n ],\n [\n -166.376953125,\n 68.942606818121\n ],\n [\n -166.376953125,\n 68.26938680456564\n ],\n [\n -163.30078125,\n 66.86108230224609\n ],\n [\n -161.982421875,\n 66.47820814385636\n ],\n [\n -163.564453125,\n 66.08936427047088\n ],\n [\n -163.564453125,\n 66.6181218846659\n ],\n [\n -165.76171875,\n 66.40795547978848\n ],\n [\n -168.0908203125,\n 65.69447579373418\n ],\n [\n -166.55273437499997,\n 65.14611484756372\n ],\n [\n -166.904296875,\n 65.05360170595502\n ],\n [\n -166.3330078125,\n 64.41592147626879\n ],\n [\n -162.861328125,\n 64.39693778132846\n ],\n [\n -160.927734375,\n 64.90491004905083\n ],\n [\n -161.0595703125,\n 64.47279382008166\n ],\n [\n -161.4990234375,\n 64.49172504435471\n ],\n [\n -160.8837890625,\n 63.87939001720202\n ],\n [\n -161.1474609375,\n 63.470144746565424\n ],\n [\n -162.6416015625,\n 63.64625919492172\n ],\n [\n -163.212890625,\n 63.05495931065107\n ],\n [\n -164.2236328125,\n 63.37183226679281\n ],\n [\n -166.1572265625,\n 61.75233128411639\n ],\n [\n -165.3662109375,\n 60.54377524118842\n ],\n [\n -167.431640625,\n 60.326947742998414\n ],\n [\n -167.255859375,\n 59.866883195210214\n ],\n [\n -165.8935546875,\n 59.7563950493563\n ],\n [\n -162.68554687499997,\n 59.734253447591364\n ],\n [\n -162.3779296875,\n 60.174306261926034\n ],\n [\n -161.806640625,\n 59.46740794183739\n ],\n [\n -162.0263671875,\n 59.108308258604964\n ],\n [\n -161.806640625,\n 58.768200159239576\n ],\n [\n -162.20214843749997,\n 58.65408464530598\n ],\n [\n -160.83984375,\n 58.44773280389084\n ],\n [\n -159.9609375,\n 58.6769376725869\n ],\n [\n -159.08203125,\n 58.309488840677645\n ],\n [\n -156.88476562499997,\n 58.92733441827545\n ],\n [\n -157.5,\n 58.516651799363785\n ],\n [\n -157.8076171875,\n 57.61010702068388\n ],\n [\n -161.54296875,\n 56.022948079627454\n ],\n [\n -168.6181640625,\n 53.4357192066942\n ],\n [\n -174.9462890625,\n 52.26815737376817\n ],\n [\n -178.2421875,\n 51.83577752045248\n ],\n [\n -173.1884765625,\n 51.590722643120145\n ],\n [\n -162.5537109375,\n 54.23955053156177\n ],\n [\n -155.302734375,\n 55.52863052257191\n ],\n [\n -151.4794921875,\n 57.51582286553883\n ],\n [\n -146.9970703125,\n 60.08676274626006\n ],\n [\n -145.546875,\n 60.21799073323445\n ],\n [\n -144.228515625,\n 59.689926220143356\n ],\n [\n -142.3828125,\n 59.93300042374631\n ],\n [\n -138.3837890625,\n 58.83649009392136\n ],\n [\n -135.6591796875,\n 56.31653672211301\n ],\n [\n -133.2421875,\n 54.521081495443596\n ],\n [\n -130.67138671875,\n 54.686534234529695\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.796875,\n 44.902577996288876\n ],\n [\n -67.67578124999999,\n 45.583289756006316\n ],\n [\n -67.939453125,\n 47.57652571374621\n ],\n [\n -69.2578125,\n 47.338822694822\n ],\n [\n -71.19140625,\n 45.27488643704891\n ],\n [\n -75.146484375,\n 44.96479793033101\n ],\n [\n -78.046875,\n 43.644025847699496\n ],\n [\n -79.1015625,\n 43.51668853502906\n ],\n [\n -79.1015625,\n 42.87596410238256\n ],\n [\n -82.68310546875,\n 41.65649719441145\n ],\n [\n -83.14453125,\n 42.049292638686836\n ],\n [\n -83.07861328125,\n 42.374778361114195\n ],\n [\n -82.529296875,\n 42.601619944327965\n ],\n [\n -82.24365234375,\n 43.6599240747891\n ],\n [\n -82.41943359375,\n 45.058001435398275\n ],\n [\n -83.60595703125,\n 45.85941212790755\n ],\n [\n -83.49609375,\n 46.027481852486645\n ],\n [\n -83.7158203125,\n 46.164614496897094\n ],\n [\n -83.95751953125,\n 46.07323062540835\n ],\n [\n -84.24316406249999,\n 46.558860303117164\n ],\n [\n -84.72656249999999,\n 46.558860303117164\n ],\n [\n -84.90234375,\n 46.92025531537451\n ],\n [\n -88.41796875,\n 48.3416461723746\n ],\n [\n -89.3408203125,\n 47.96050238891509\n ],\n [\n -90.76904296874999,\n 48.122101028190805\n ],\n [\n -90.87890625,\n 48.22467264956519\n ],\n [\n -91.51611328125,\n 48.10743118848039\n ],\n [\n -92.2412109375,\n 48.37084770238366\n ],\n [\n -92.39501953125,\n 48.23930899024907\n ],\n [\n -92.94433593749999,\n 48.61838518688487\n ],\n [\n -93.44970703125,\n 48.63290858589535\n ],\n [\n -94.7021484375,\n 48.748945343432936\n ],\n [\n -94.833984375,\n 49.23912083246698\n ],\n [\n -95.1416015625,\n 49.396675075193976\n ],\n [\n -95.20751953125,\n 49.009050809382046\n ],\n [\n -123.22265625000001,\n 48.99463598353405\n ],\n [\n -123.0908203125,\n 48.80686346108517\n ],\n [\n -123.24462890625,\n 48.66194284607006\n ],\n [\n -123.1787109375,\n 48.32703913063476\n ],\n [\n -124.78271484375,\n 48.472921272487824\n ],\n [\n -124.93652343749999,\n 48.16608541901253\n ],\n [\n -124.365234375,\n 46.58906908309182\n ],\n [\n -124.541015625,\n 44.15068115978094\n ],\n [\n -124.93652343749999,\n 42.69858589169842\n ],\n [\n -124.541015625,\n 41.22824901518529\n ],\n [\n -124.73876953125,\n 40.43022363450862\n ],\n [\n -124.03564453125,\n 39.35129035526705\n ],\n [\n -124.01367187499999,\n 38.8225909761771\n ],\n [\n -122.05810546875,\n 36.12012758978146\n ],\n [\n -120.95947265624999,\n 34.88593094075317\n ],\n [\n -120.80566406250001,\n 34.08906131584994\n ],\n [\n -118.21289062499999,\n 32.2313896627376\n ],\n [\n -117.22412109375,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.713355353177555\n ],\n [\n -114.78515624999999,\n 32.491230287947594\n ],\n [\n -110.98388671874999,\n 31.3348710339506\n ],\n [\n -108.21533203125,\n 31.297327991404266\n ],\n [\n -108.2373046875,\n 31.765537409484374\n ],\n [\n -106.435546875,\n 31.765537409484374\n ],\n [\n -104.9853515625,\n 30.600093873550072\n ],\n [\n -104.47998046875,\n 29.592565403314087\n ],\n [\n -103.20556640625,\n 28.94086176940557\n ],\n [\n -102.65625,\n 29.76437737516313\n ],\n [\n -102.3486328125,\n 29.84064389983441\n ],\n [\n -101.49169921875,\n 29.7453016622136\n ],\n [\n -100.83251953125,\n 29.267232865200878\n ],\n [\n -100.30517578125,\n 28.246327971048842\n ],\n [\n -99.60205078124999,\n 27.586197857692664\n ],\n [\n -99.47021484375,\n 27.31321389856826\n ],\n [\n -99.228515625,\n 26.52956523826758\n ],\n [\n -98.2177734375,\n 26.05678288577881\n ],\n [\n -97.75634765625,\n 26.03704188651584\n ],\n [\n -97.44873046875,\n 25.839449402063185\n ],\n [\n -97.20703125,\n 25.93828707492375\n ],\n [\n -96.8994140625,\n 26.194876675795218\n ],\n [\n -96.78955078125,\n 27.858503954841247\n ],\n [\n -93.75732421875,\n 29.420460341013133\n ],\n [\n -90.2197265625,\n 28.998531814051795\n ],\n [\n -88.22021484375,\n 29.05616970274342\n ],\n [\n -87.91259765625,\n 30.14512718337613\n ],\n [\n -86.5283203125,\n 30.183121842195515\n ],\n [\n -85.2978515625,\n 29.49698759653577\n ],\n [\n -84.13330078125,\n 29.80251790576445\n ],\n [\n -82.81494140625,\n 28.555576049185973\n ],\n [\n -83.21044921875,\n 27.800209937418252\n ],\n [\n -82.77099609375,\n 26.941659545381516\n ],\n [\n -82.08984375,\n 25.878994400196202\n ],\n [\n -81.5625,\n 25.264568475331583\n ],\n [\n -82.28759765625,\n 24.467150664739002\n ],\n [\n -82.0458984375,\n 24.046463999666567\n ],\n [\n -80.6396484375,\n 24.56710835257599\n ],\n [\n -79.78271484375,\n 25.34402602913433\n ],\n [\n -79.60693359375,\n 27.27416111737468\n ],\n [\n -80.68359375,\n 30.713503990354965\n ],\n [\n -80.66162109375,\n 31.50362930577303\n ],\n [\n -76.81640625,\n 34.07086232376631\n ],\n [\n -75.16845703124999,\n 35.263561862152095\n ],\n [\n -75.498046875,\n 37.055177106660814\n ],\n [\n -73.58642578125,\n 39.90973623453719\n ],\n [\n -71.3671875,\n 40.84706035607122\n ],\n [\n -69.63134765625,\n 40.9964840143779\n ],\n [\n -70.0048828125,\n 42.342305278572816\n ],\n [\n -70.3564453125,\n 42.89206418807337\n ],\n [\n -67.2802734375,\n 44.37098696297173\n ],\n [\n -67.0166015625,\n 44.69989765840318\n ],\n [\n -66.796875,\n 44.902577996288876\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.56640625,\n 18.771115062337024\n ],\n [\n -154.68749999999997,\n 19.642587534013032\n ],\n [\n -156.9287109375,\n 21.453068633086783\n ],\n [\n -159.521484375,\n 22.43134015636061\n ],\n [\n -160.5322265625,\n 21.983801417384697\n ],\n [\n -159.9609375,\n 21.207458730482642\n ],\n [\n -158.291015625,\n 20.92039691397189\n ],\n [\n -156.97265625,\n 19.932041306115536\n ],\n [\n -155.9619140625,\n 18.8543103618898\n ],\n [\n -155.56640625,\n 18.771115062337024\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.060546875,\n 18.020527657852337\n ],\n [\n -66.2255859375,\n 17.916022703877665\n ],\n [\n -65.6103515625,\n 17.97873309555617\n ],\n [\n -65.2587890625,\n 18.124970639386515\n ],\n [\n -65.5224609375,\n 18.458768120015126\n ],\n [\n -66.11572265625,\n 18.542116654448996\n ],\n [\n -66.95068359374999,\n 18.60460138845525\n ],\n [\n -67.34619140625,\n 18.542116654448996\n ],\n [\n -67.2802734375,\n 17.99963161491187\n ],\n [\n -67.060546875,\n 18.020527657852337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Program Coordinator, Water Resources Research Act Program
U.S. Geological Survey, Water Mission Area
12201 Sunrise Valley Drive, MS 432
Reston, Virginia 20192
The noble gas temperature climate proxy is an established tool that has previously been applied to determine the source of groundwater recharge, however, unanswered questions remain. In fractured media (e.g., volcanic islands) recharge can be so rapid that groundwater is significantly depleted in heavy noble gases, indicating that the water has retained noble gas concentrations from higher elevations. Previous studies of rain samples have confirmed a match to patterns seen in fractured-rock groundwater for heavy noble gases along with a significant helium excess. Snow has been shown to be a credible source for both the helium excess and the observed heavy noble gas pattern. Here, liquid cloud water samples were collected at two mountainous sites and analyzed for noble gas concentrations. A pattern like that of rainwater was found. However, an analysis of diffusive uptake of noble gases into cloud water demonstrates that droplets of 1 mm diameter and smaller should be in constant solubility equilibrium with the atmosphere. To explain this, we present a novel hypothesis that relies on the assumption that liquid water consists of two types of water molecule clusters bounded by hydrogen bonds: a low-density ice-like structure and a high-density condensed structure. In this model, the pressure gradient near the surface of a droplet resulting from surface tension could allow for the formation of a surface layer that is rich in ice-like low density clusters. This can explain both the helium excess and the heavy noble gas depletion seen in the samples.
Indicator species are species which can be monitored as an index to measure the overall health of an ecosystem. Crocodylians have been shown to be good indicators of wetland condition as they respond to changes in hydrology, can be efficiently monitored, and are a key part of ecosystem trophic relationships. Eye shine surveys at night are a standard method used to sample alligators, but because some individuals that are present in a study area may go undetected and the proportion of individuals counted is not constant over time, appropriate modeling is required to convert counts to estimates of abundance. We analyzed 13 years of American alligator (Alligator mississippiensis) survey count data from South Florida using an N-mixture model. Alligator abundance estimates were assigned to quartiles that were then represented as color coded categories of red, yellow, or green to provide a straightforward rating of Everglades restoration based on familiar stoplight coloring. These results were then compared to a previously used method in which unadjusted counts of these same data were assigned to color coded quartile categories. Water depth played a major role in the detection probability of alligators and the stoplight colors between the two methods matched 76% of the time. This suggests that the original stoplight score method provided a good overall snapshot of the trends in alligator abundance in the Everglades; however, the hierarchical models estimate abundance and trends of alligator abundance by incorporating detection probability thus providing unbiased estimates of abundance.
Effective conservation for listed migratory species requires an understanding of how drivers of population decline vary spatially and temporally, as well as knowledge of range-wide connectivity between breeding and nonbreeding areas. Environmental conditions distant from breeding areas can have lasting effects on the demography of migratory species, yet these consequences are often the least understood. Our objectives were to 1) evaluate associations between survival and extreme environmental disturbances at nonbreeding areas, including hurricanes, harmful algal blooms, and oil spills, and 2) estimate migratory connectivity between breeding and nonbreeding areas of midcontinental piping plovers (Charadrius melodus). We used capture and resighting data from 5067 individuals collected between 2002 and 2019 from breeding areas across the midcontinent, and nonbreeding areas throughout the Gulf of Mexico and southern Atlantic coasts of North America. We developed a hidden Markov multistate model to estimate seasonal survival and account for unobservable geographic locations. Hurricanes and harmful algal blooms were negatively associated with nonbreeding season survival, but we did not detect a similarly negative relationship with oil spills. Our results indicated that individuals from separate breeding areas mixed across nonbreeding areas with low migratory connectivity. Mixing among individuals in the nonbreeding season may provide a buffering effect against impacts of extreme events on any one breeding region. Our results suggest that understanding migratory connectivity and linking seasonal threats to population dynamics can better inform conservation strategies for migratory shorebirds.